18 results on '"Toh, Kim-Chuan"'
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2. Solving graph equipartition SDPs on an algebraic variety
3. An inexact projected gradient method with rounding and lifting by nonlinear programming for solving rank-one semidefinite relaxation of polynomial optimization
4. Doubly nonnegative relaxations for quadratic and polynomial optimization problems with binary and box constraints
5. On the equivalence of inexact proximal ALM and ADMM for a class of convex composite programming
6. An efficient Hessian based algorithm for solving large-scale sparse group Lasso problems
7. On the efficient computation of a generalized Jacobian of the projector over the Birkhoff polytope
8. On the R-superlinear convergence of the KKT residuals generated by the augmented Lagrangian method for convex composite conic programming
9. A block symmetric Gauss–Seidel decomposition theorem for convex composite quadratic programming and its applications
10. Spectral operators of matrices
11. Max-norm optimization for robust matrix recovery
12. An efficient inexact symmetric Gauss–Seidel based majorized ADMM for high-dimensional convex composite conic programming
13. A Lagrangian–DNN relaxation: a fast method for computing tight lower bounds for a class of quadratic optimization problems
14. A semismooth Newton-CG based dual PPA for matrix spectral norm approximation problems
15. A Schur complement based semi-proximal ADMM for convex quadratic conic programming and extensions
16. An introduction to a class of matrix cone programming
17. An implementable proximal point algorithmic framework for nuclear norm minimization
18. A block coordinate gradient descent method for regularized convex separable optimization and covariance selection
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